{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2c1d3c1b-6200-4e7e-a3cd-c59a001aae25",
   "metadata": {},
   "source": [
    "# 预测nii，并保存 取中间部分"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1389e0d-c4c5-4f38-a810-42707a6558ac",
   "metadata": {},
   "source": [
    "## 导入库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4a0a1f93-7f21-420e-95a7-548bc934ab64",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ['TF_FORCE_GPU_ALLOW_GROWTH']='true'\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"1\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "effa0e67-2ef4-4145-bf8a-61f984ef9113",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "from pathlib import Path\n",
    "\n",
    "from nibabel.viewers import OrthoSlicer3D\n",
    "from nibabel import nifti1\n",
    "import nibabel as nib\n",
    "\n",
    "import glob\n",
    "import random\n",
    "\n",
    "from tensorflow.keras import layers\n",
    "from keras.models import load_model\n",
    "from tensorflow.keras.models import Model\n",
    "\n",
    "import surface_distance as surfdist\n",
    "\n",
    "from datetime import datetime\n",
    "import time\n",
    "from datetime import timedelta"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b0bebb4-d168-4248-beee-1cdf64a3889e",
   "metadata": {},
   "source": [
    "## 初始参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6d4e7ded-d13e-4d02-b9ee-817ddbe62dbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "#CT图用线性插值\n",
    "#mask用最邻近插值\n",
    "\n",
    "#截取窗宽窗位\n",
    "window_width = 330\n",
    "window_center = 40\n",
    "\n",
    "#网络图像大小\n",
    "SIZE = 128\n",
    "deepz = 64\n",
    "\n",
    "#取ROi\n",
    "py = 0.2\n",
    "px = 0.25\n",
    "prange = 0.5\n",
    "\n",
    "#取切片\n",
    "p130 = 0.38\n",
    "p131 = 0.42\n",
    "p150 = 0.55\n",
    "p180 = 0.6"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "030cd69e-eeb8-4ea6-8dde-3817eef5a5bb",
   "metadata": {},
   "source": [
    "# model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "545835a2-9260-4f2d-9cb1-d21f9ccc7934",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-04-23 09:57:55.943745: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-04-23 09:57:55.983674: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-04-23 09:57:55.984796: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-04-23 09:57:55.986423: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2022-04-23 09:57:55.991078: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-04-23 09:57:55.992069: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-04-23 09:57:55.993033: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-04-23 09:57:56.451209: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-04-23 09:57:56.452240: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-04-23 09:57:56.453199: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-04-23 09:57:56.454095: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n",
      "2022-04-23 09:57:56.454152: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 22306 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:08:00.0, compute capability: 8.6\n"
     ]
    }
   ],
   "source": [
    "num_classes = 3\n",
    "\n",
    "inputs = layers.Input(shape=(64,128,128,1),name='vnet_input')\n",
    "conv1 = layers.Conv3D(16,5,padding='same',name='conv1')(inputs)\n",
    "conv1 = layers.PReLU(name='conv1_prelu')(conv1) # activation\n",
    "\n",
    "concat1 = layers.Concatenate(name='concat1')([inputs,conv1])\n",
    "down_conv1 = layers.Conv3D(32,2,2,name='down_conv_1')(concat1)\n",
    "down_conv1 = layers.PReLU(name='down_conv1_prelu')(down_conv1)\n",
    "\n",
    "conv2_1 = layers.Conv3D(32,5,padding='same',name='conv2_1')(down_conv1)\n",
    "conv2_1 = layers.PReLU(name='conv2_1_prelu')(conv2_1)\n",
    "conv2_2 = layers.Conv3D(32,5,padding='same',name='conv2_2')(conv2_1)\n",
    "conv2_2 = layers.PReLU(name='conv2_2_prelu')(conv2_2)\n",
    "\n",
    "concat2 = layers.Concatenate(name='concat2')([down_conv1,conv2_2])\n",
    "down_conv2 = layers.Conv3D(64,2,2,name='down_conv2')(concat2)\n",
    "down_conv2 = layers.PReLU(name='down_conv2_prelu')(down_conv2)\n",
    "\n",
    "conv3_1 = layers.Conv3D(64,5,padding='same',name='conv3_1')(down_conv2)\n",
    "conv3_1 = layers.PReLU(name='conv3_1_prelu')(conv3_1)\n",
    "conv3_2 = layers.Conv3D(64,5,padding='same',name='conv3_2')(conv3_1)\n",
    "conv3_2 = layers.PReLU(name='conv3_2_prelu')(conv3_2)\n",
    "conv3_3 = layers.Conv3D(64,5,padding='same',name='conv3_3')(conv3_2)\n",
    "conv3_3 = layers.PReLU(name='conv3_3_prelu')(conv3_3)\n",
    "\n",
    "concat3 = layers.Concatenate(name='concat3')([conv3_3,down_conv2])\n",
    "down_conv3 = layers.Conv3D(128,2,2,name='down_conv3')(concat3)\n",
    "down_conv3 = layers.PReLU(name='down_conv3_prelu')(down_conv3)\n",
    "\n",
    "conv4_1 = layers.Conv3D(128,5,padding='same',name='conv4_1')(down_conv3)\n",
    "conv4_1 = layers.PReLU(name='conv4_1_prelu')(conv4_1)\n",
    "conv4_2 = layers.Conv3D(128,5,padding='same',name='conv4_2')(conv4_1)\n",
    "conv4_2 = layers.PReLU(name='conv4_2_prelu')(conv4_2)\n",
    "conv4_3 = layers.Conv3D(128,5,padding='same',name='conv4_3')(conv4_2)\n",
    "conv4_3 = layers.PReLU(name='conv4_3_prelu')(conv4_3)\n",
    "\n",
    "concat4 = layers.Concatenate(name='concat4')([down_conv3,conv4_3])\n",
    "down_conv4 = layers.Conv3D(256,2,2,name='down_conv4')(concat4)\n",
    "down_conv4 = layers.PReLU(name='down_conv4_prelu')(down_conv4)\n",
    "\n",
    "conv5_1 = layers.Conv3D(256,5,padding='same',name='conv5_1')(down_conv4)\n",
    "conv5_1 = layers.PReLU(name='conv5_1_prelu')(conv5_1)\n",
    "conv5_2 = layers.Conv3D(256,5,padding='same',name='conv5_2')(conv5_1)\n",
    "conv5_2 = layers.PReLU(name='conv5_2_prelu')(conv5_2)\n",
    "conv5_3 = layers.Conv3D(256,5,padding='same',name='conv5_3')(conv5_2)\n",
    "conv5_3 = layers.PReLU(name='conv5_3_prelu')(conv5_3)\n",
    "\n",
    "concat5 = layers.Concatenate(name='concat5')([down_conv4,conv5_3])\n",
    "up_conv5 = layers.Conv3DTranspose(256,2,2,name='up_conv5')(concat5)\n",
    "up_conv5 = layers.PReLU(name='up_conv5_prelu')(up_conv5)\n",
    "\n",
    "conv6 = layers.Concatenate(name='conv6')([up_conv5,concat4])\n",
    "conv6_1 = layers.Conv3D(256,5,padding='same',name='conv6_1')(conv6)\n",
    "conv6_1 = layers.PReLU(name='conv6_1_prelu')(conv6_1)\n",
    "conv6_2 = layers.Conv3D(256,5,padding='same',name='conv6_2')(conv6_1)\n",
    "conv6_2 = layers.PReLU(name='conv6_2_prelu')(conv6_2)\n",
    "conv6_3 = layers.Conv3D(256,5,padding='same',name='conv6_3')(conv6_2)\n",
    "conv6_3 = layers.PReLU(name='conv6_3_prelu')(conv6_3)\n",
    "\n",
    "concat6 = layers.Concatenate(name='concat6')([up_conv5,conv6_3])\n",
    "up_conv6 = layers.Conv3DTranspose(128,2,2,name='up_conv6')(concat6)\n",
    "up_conv6 = layers.PReLU(name='up_conv6_prelu')(up_conv6)\n",
    "\n",
    "conv7 = layers.Concatenate(name='conv7')([up_conv6,concat3])\n",
    "\"\"\" 3次卷积 \"\"\"\n",
    "for i in range(1,4):\n",
    "    conv7 = layers.Conv3D(128,5,padding='same',name=f'conv7_{i}')(conv7)\n",
    "    conv7 = layers.PReLU(name=f'conv7_{i}_prelu')(conv7)\n",
    "\n",
    "concat7 = layers.Concatenate(name='concat7')([up_conv6,conv7])\n",
    "up_conv7 = layers.Conv3DTranspose(64,2,2,name='up_conv7')(concat7)\n",
    "up_conv7 = layers.PReLU(name='up_conv7_prelu')(up_conv7)\n",
    "\n",
    "conv8 = layers.Concatenate(name='conv8')([up_conv7,concat2])\n",
    "\"\"\" 2次卷积 \"\"\"\n",
    "for i in range(1,3):\n",
    "    conv8 = layers.Conv3D(64,5,padding='same',name=f'conv8_{i}')(conv8)\n",
    "    conv8 = layers.PReLU(name=f'conv8_{i}_prelu')(conv8)\n",
    "\n",
    "concat8 = layers.Concatenate(name='concat8')([conv8,up_conv7])\n",
    "up_conv8 = layers.Conv3DTranspose(32,2,2,name='up_conv8')(concat8)\n",
    "up_conv8 = layers.PReLU(name='up_conv8_prelu')(up_conv8)\n",
    "\n",
    "conv9 = layers.Concatenate(name='conv9')([up_conv8,concat1])\n",
    "conv9_1 = layers.Conv3D(32,5,padding='same',name='conv9_1')(conv9)\n",
    "\n",
    "concat9 = layers.Concatenate(name='concat9')([conv9_1,up_conv8])\n",
    "conv10 = layers.Conv3D(num_classes,1,1,name='out',activation=\"softmax\")(concat9)\n",
    "\n",
    "model = Model(inputs=inputs,outputs=conv10,name='myTf2_vnet')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ae674b2-ab2a-4a4e-9101-6286ba276c8c",
   "metadata": {},
   "source": [
    "# predict"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbd501aa-d024-4b3e-b9fe-3ee43f3e4d73",
   "metadata": {},
   "source": [
    "## 加载模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "568eb1b6-0e9b-4baf-80e4-be86c4cdbe24",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.load_weights('../data0_link/yhs_data/NPC_history/model/vnet_5_32-40.hdf5')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18156a36-254f-4cc4-a2b4-04ebfc5846fd",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 取nii\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0a8f4682-5363-429a-8f46-ab0bf652eb9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#搜索数据\n",
    "all_data= glob.glob(os.path.join(\"../data0_link/yhs_data/NPC_Dataset/test/*.nii.gz\"))#15\n",
    "print(len(all_data))\n",
    "\n",
    "#all_data= all_data[:2]\n",
    "#print(len(all_data))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "baf81439-51f4-4bcc-9bfe-d38be4acc787",
   "metadata": {},
   "source": [
    "# 输入nii_dir,并预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f934ef5a-3786-4dc4-9549-df4eab84f677",
   "metadata": {},
   "outputs": [],
   "source": [
    "#输入nii_dir\n",
    "#返回z,w,h,c的img、affine映射、ROI_xy(x1,x2,y1,y2,all_range,slice1,z)\n",
    "def process(nii_dir):\n",
    "\n",
    "    #读\n",
    "    ori_data = nib.load(nii_dir)\n",
    "    #获取映射信息\n",
    "    img_affine  = ori_data.affine\n",
    "    \n",
    "    ROI_xy = np.zeros(7).astype(np.int32)\n",
    "    ROI_xy[6] = ori_data.dataobj.shape[2]\n",
    "    #获取slice\n",
    "    if ori_data.dataobj.shape[2] >= 180:\n",
    "        ROI_xy[5] = np.floor(ori_data.dataobj.shape[2]*p180).astype(np.int32) - 16\n",
    "    elif ori_data.dataobj.shape[2] >= 150:\n",
    "        ROI_xy[5] = np.floor(ori_data.dataobj.shape[2]*p150).astype(np.int32) - 16\n",
    "    elif ori_data.dataobj.shape[2] > 130:\n",
    "        ROI_xy[5] = np.floor(ori_data.dataobj.shape[2]*p131).astype(np.int32) - 16\n",
    "    else:\n",
    "        ROI_xy[5] = np.floor(ori_data.dataobj.shape[2]*p130).astype(np.int32) - 16\n",
    "        \n",
    "    #获取ROI\n",
    "    all_range = ori_data.dataobj.shape[0] \n",
    "    ROI_xy[4] = all_range   \n",
    "    ROI_xy[0] = np.floor(all_range*px).astype(np.int32)\n",
    "    ROI_xy[1] = np.floor(all_range*(px+prange)).astype(np.int32)\n",
    "    ROI_xy[2] = np.floor(all_range*py).astype(np.int32)\n",
    "    ROI_xy[3] = np.floor(all_range*(py+prange)).astype(np.int32)\n",
    "    process_img = ori_data.dataobj[ROI_xy[0]:ROI_xy[1],ROI_xy[2]:ROI_xy[3],ROI_xy[5]:ROI_xy[5]+64]\n",
    "    \n",
    "    #resize\n",
    "    process_img = tf.image.resize(process_img,(SIZE,SIZE),method='bilinear')\n",
    "    \n",
    "    #窗宽窗位\n",
    "    process_img = process_img.numpy()\n",
    "    min_window = float(window_center) - 0.5*float(window_width)\n",
    "    process_img = (process_img - min_window) / float(window_width)\n",
    "    process_img[process_img < 0] = 0\n",
    "    process_img[process_img > 1] = 1\n",
    "    \n",
    "    #均值方差归一化\n",
    "    process_img = (process_img - np.mean(process_img)) / np.std(process_img)\n",
    "    \n",
    "    #增加一个维度（w,h,z,c）\n",
    "    process_img = tf.expand_dims(process_img,-1)\n",
    "    \n",
    "    #转换维度顺序 （w,h,z,c）---(z,w,h,c)\n",
    "    process_img = tf.transpose(process_img, perm=[2,0,1,3])\n",
    "        \n",
    "    return process_img, img_affine, ROI_xy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59614d4e-b059-4357-a235-46495e0f72e4",
   "metadata": {},
   "source": [
    "## 遍历预测/保存分割结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6811661f-a7ee-4a5f-b6c2-30aeaf762f7f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "roi= [ 256  768  204  716 1024   33  129]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-04-23 09:58:15.487060: I tensorflow/stream_executor/cuda/cuda_dnn.cc:368] Loaded cuDNN version 8300\n",
      "2022-04-23 09:58:16.572115: I tensorflow/core/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------finish: 0086\n",
      "roi= [ 256  768  204  716 1024   31  125]\n",
      "------------------------finish: 0081\n",
      "roi= [ 256  768  204  716 1024   33  129]\n",
      "------------------------finish: 0090\n",
      "roi= [ 256  768  204  716 1024   30  122]\n",
      "------------------------finish: 0087\n",
      "roi= [ 256  768  204  716 1024   33  130]\n",
      "------------------------finish: 0080\n",
      "roi= [ 256  768  204  716 1024   30  122]\n",
      "------------------------finish: 0082\n",
      "roi= [ 256  768  204  716 1024   39  132]\n",
      "------------------------finish: 0085\n",
      "roi= [ 256  768  204  716 1024   32  128]\n",
      "------------------------finish: 0079\n",
      "roi= [ 256  768  204  716 1024   29  119]\n",
      "------------------------finish: 0083\n",
      "roi= [128 384 102 358 512  81 177]\n",
      "------------------------finish: 0088\n",
      "roi= [ 256  768  204  716 1024   30  123]\n",
      "------------------------finish: 0089\n",
      "roi= [ 256  768  204  716 1024   27  114]\n",
      "------------------------finish: 0092\n",
      "roi= [ 256  768  204  716 1024   29  119]\n",
      "------------------------finish: 0091\n",
      "roi= [ 256  768  204  716 1024   29  120]\n",
      "------------------------finish: 0093\n",
      "roi= [ 256  768  204  716 1024   26  112]\n",
      "------------------------finish: 0084\n",
      "------------------------all-finish------------------------------\n",
      "time usage: 0:02:26\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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G8PUwzT3f//73yeVy5HI5lTRmrgLM0g/FYlFVDrXZbEgpefvtt/nWt75FT08PLpeLXbt20dDQgNVqVXf/ra2t7N27l87OTgqFAuFwmMHBQVKp1KJVjDnpSylpa2ujq6uLXbt24Xa7AQiFQjgcDoQQ9Pb2EgqF2L9//3V/2xJyV8ZVo1kK5OVhfrvv6au2D+QTPPONf4I4enoFpFodLEfZiKeA78w//w7w9+/0gIFAQE20tbW17Nu3jy1bttDa2sqePXt4+OGHVQLXzZBIJHjllVc4fvw4g4ODpNNpYrEYvb29XLhwgcHBQYaGhigUClgsFqSUykcgpVSF4I4dO0Y8Hqe6upq9e/eqEhNWqxXDMPD5fNhsNpqbm6mqqiIajSqfRbFYVHZ/KDu6vV4vwWCQzs5ODhw4gMPhUBFJphwDAwMqSe0us+TjqtEsFaVMBudvu3j4zGdJlcqRdkVZ4rG3fov6/3Jctwm9AXfqE5DAESGEBP5YSvkCEJJSXpl/fwIIXeuDQojngJuq5rVlyxZ2795NKpWira2NYDBIfX09e/fupa6u7pYETqfTvPXWW4yPj1NVVUUsFsNisZBOpxkdHWVwcJBMJsPhw4fJZrMUi+WKkwsbx7S3t9Pc3IwQQiWaNTQ0cPDgQd566y0VoSSlVL6Buro6hBBMT0+ronJWqxUom5hSqRTV1dW4XC7sdrsqWNfX10dzc7NqcjM1NcWGDRtoaGigv7//ln77LbAk4+rEvVzyaTRXUTp7Ec/Pe/jF1/4eP9j8Gl3v/UPu/adDFEs3VzV2vXKnSuAhKeWYEKIe+LEQYlEMo5RSzk8kVzE/sbwAcL19oLwKCIVCTE1N0draSiAQ4ODBg2zYsEHF/F/j2MTjcfr7+8nn86TTaVXtE1DlHKqrq1Xdf5/Px7333ktzczPxeJxgMKhCP81jHjhwgH379qnuY6aZyKSlpYVHHnmE119/XbWoBJiYmCAYDNLc3EyxWEQIQTKZVNnELpcLm81GsVhUysbhcFBbW8v58+cxDINgMMjY2Bj5fJ4rV66wa9cuRkdHl8tJvCTj6hcBffuluauUkkli39hO5+Nf4t6v91Gc0R38Po47UgJSyrH5xykhxA+Ag8CkEKJRSnlFCNEITN3u8QOBAB0dHQC43W6cTif79u1j586d15JFhXRevHiRRCJBMpkkHo+TyWRUhq/H41EtIs0J16wgarFYCAQCVFdXq97Bdrsdv9/Pk08+SUdHB0IIJiYmmJubY2Zmht27dyv7PZQVwcMPP8wPfvADpqamOHHiBPF4nJ07dxIMBhFCqEzjbDar7vgdDgeFQoFCoaDMT9XV1ezatYuenh4Mw1BtLycnJ+no6FAKaqlZ7nHVaJYT56vv0vlqOapB8/Hctk9ACOERQvjM58ATwFngFeCZ+d2eAV6+3e84dOgQW7ZsUVU+N2/ezOHDh6+574ULFzhy5AivvvoqIyMjRCIRotEoMzMzJJNJEokEhUKBeDyuTCumqce04wOqeqdpokkkEuzatYsNGzaoUNOGhgY6OztVNu9H6ezsZOPGjXR3dzM4OKhWG6YvAcolL8zw0Ww2SzQaXZQfYPYqqK+vV7kLZvG7RCJBLBajtbX1dk/tjbAs97hqNJrK4U5WAiHgB/MTowH8Tynlj4QQ7wHfE0L8CjAEXO2yv0lMZ6zL5cLlcvEzP/Mz19zv7NmzHD16lMnJSRWvb5aDyOfzAKotpBnGaU78pVJJlWwwsVqtqjm8z+dj8+bN1/xec1K+Fp/85Cf5kz/5E4aHh4lEIpw7d25RnkBDQwN2ux3DMJSSqq6uxjAMldBms9lIJpOEQiGVA2FGL4XDYbq6uvjggw9u69zeAAP46XKOq0ajqRxuWwlIKS8Du6+xPQw8didCAbhcLpLJJB6Ph9raWkKhEMFgcNE+uVyOixcvcu7cOSKRiCrSZvYAMDuEVVVVUSqVVGSOw+FYNHk7nU4VsQPlVYDZbMZ8fS0sFguZTIYf/vCH+P1+HnjgAaqrq4Fyr4Fvf/vbfPazn6Wvr49kMsng4CCBQIAtW7YQiUTYsmULPp8PKSVzc3OquqhZl6hYLKqSE/l8nlQqpXodRKNRNm/ejMViWereA7kFYaGKpRpXjUZTWVRkZzEhBDt37sTpdOLz+VTzlo82gxkdHeXs2bPMzMyQz+cplUrK9m+2hqyvr8fpdKrPmvZ2c+I0Y/sXmoPM3ADTdn/y5EkGBweJRqOL5Jybm+Pdd98lEokwNDTEpUuXlLknkUgQCoX4+te/DpRzExwOB06nk3g8zsjIiMoanp2dpVgsqg5nZjio+dtTqZSS1fRfpNNp8vm8WlloNCY/la9xTB7hHfljjss3zc1WIcSPhRC98481AKLMN4UQfUKIHiFE18pJrlkJKrJshFmd02q14vf7cbvdbNmyZZEjNBqNcv78eRVRY8bNSykxDEO1ijQMQ2XmQlnBLOwpXCqVyOfzKrN3dnZW9SS2Wq1kMhkuXLjAxMQETU1NtLW14Xa7SSaTjI+Pc/nyZcbHxxFC0N3dTVdXF4ZhkEgkCIfDhEIhWltbmZqaUiam2dlZcrkc0WiUqqoqRkZG2LdvH16vl3w+jxCCQqGA3W6nuroau91OJpNRjmrTn2H2Nh4fH1+RcdJULvs4jF04Fm5qBP5CSvl7QojnKSf7/Q7waaBz/u8+4Fvzj5p1QsWuBKBcJsLtdquyzAtrAF28eHGR2cblcimFYZpIzBh9gGKxSLFYVFnAFouFfD6vQkjn5uaYnp7mO9/5Di+99BLHjh0jnU6rxvCtra089NBDdHR00NTUhMvlIhaLqSxju91OLBbj5ZdfplQqUVdXx9zcHNu3b+fzn/887e3ttLW1YbfbKRaLGIbBwMAAb775JuPj44TDYaWMzOOZDXBaWloIBoP4fD4KhYLyReRyOWV+0mg+hmqunez3FPBdWeYdoHo++kuzTqhIJWBW1wRUTZ9EIqHu5gFV4yeTySxy7JqTcjQaJZ/Pq7DLTCaj7qDNu+2FysKM8onFYiQSCd555x0CgQBer5fm5mYef/xxVZPIjFTq6OggkUiolYgQgpGREc6dO4fVaiUajWKz2WhsbKS6ulqFhNbX16vVQiKRIJPJcPLkSc6fP69WP6ZvAMqhsna7XWUQmyuidDqt/BYazUJO8TbH5RuMysvmJuM6yX7NwMJOOqPz2xYhhHhOCNEthOjOowsYriUq0hwUi8WYnZ1Vd/VVVVU0NDQsWgmY0T/m5G1O7KbJJBQKkcvl1GtTaZglHRYey7yzXuhgNR2zXq+X2traa/YeNgwDi8WiJmJz4jZ9B/v27QNQk3YgEFB2fTNqyTRDwYerFTNXwNwuhMDr9VIsFqmuriaTyRCJRFQpbY1mIft5FKdwkZMZTvI2Hrn4GrlRst/10EmAa5eKXAlYLBaEECoi5loTcFVVlXLgptNp1eHLdJwuLL1gsVhUkbZSqYRhGEgp1eRsRvmYEzN8mHyWTqcZHBzk/fffX/T9puPZrAZqhm5mMhmlTEy5zXwF07Rkt9vVd5s1htxut2pDmUqllMnIDGM1XxcKBRKJBIAyG61AHSFNBeMU5VImduGkjibizAIUTDPPR5L9xoCFCSct89s064SKVAJmLH+xWCQajaqJcSGdnZ2qHHQqlWJycpKxsTFGR0fJ5XIkk0lSqRTJZFI5hs2oGzN8NJfLqeqg5oRdW1vLxo0b+fKXv6xKS6dSKXp6enjnnXdIpVLE43FOnDihOoXlcjmlRGpqati0aRMAk5OThMNhent7mZubU/uamc2zs7PE43Hm5ubI5XIEg0EaGhrweDxKJlNhmK9NRSCEYG5ujqqqKpqbr1q9a9YpRVmgIPPq+SyTeKgCiHLtZL9XgC/MRwndD8QWmI0064CKNAcBDA0N0draSjqdZnp6mjfeeEO1iISyCWfHjh3Mzs6qSbVQKKh6QlJKnE6nmmCdTidWq1XV7FlYKqJUKuF0OmlubuZLX/qSquVjmmTMZjFmJI/ZaN50SJsNYGw2G3v37qWlpQUol4Hu7u7mzTffxGazEYvFmJqaQkqpjuHxeKiurqampoaamppFUUxQbjU5Pj6uVh6mIjOVgZnYptEAZMnQwzGQIJE00EpQNIDkCvD4NZL9XgM+A/QBKeDZlZFcs1JUrBKIRqN4PB51R3/hwgVlEjGpqqpi06ZNzM3NEQgEVIcxYFGpZnPCdjqdOBwOZV4x9wNUFNHCTGLDMMjlctTX17Nnzx7q6+sXydjU1MTrr7+uHMuBQGBRrX+bzaaazdTU1BAIBIjH46RSKfx+P9u2bVP9jDdu3Kjs+/l8XsmTzWaZmJhQq4NsNquc5aYJyTyGRuMWXu7n8Wu9VZRSXpXsJ8t3HV9edsE0FUtFmoNM3n77bXw+n0oC+9u//dtFzlu73c6OHTtUDR2Px4PD4cDv96ssYUAliwkhqK6uVvs5HA5sNptKGFvoMDZr9Xg8Hrq6uq5SAAD19fU89dRT1NfX09LSwuOPP75ISU1NTfGNb3yDiYkJRkdH1Spi06ZNbNu2jYaGBtra2lTTGDMs1JQhl8sxOzuLxWIhGo0qv0ehUCCZTGK324lEIoyNaROuRqO5PSp2JQBlm3oikcButxMOh/npT39KW1sb27ZtU/sYhsFjjz1GR0cH3d3dhMNhHA6HcgybTtSF2bhmroCZEWximnTMZC2zDtGNqnX6/X7V9H5hzH40GuV3f/d3mZiYoKqqCq/XSzabxTAMmpqaaGlpUaWk0+k0iURCNY8xTVEzMzMMDw8zOzuLx+NRReZMc5DpHE+n00t/8jUazbqgopVAsVikt7eXBx98kJmZGWpqaujp6cHj8dDa2rqojMSmTZsIhUJcvnyZgYEB0um06hmwcOJfGNVjhpRmMhm1UoAPM5bz+TyGYXDq1CmefPLJq6KUEomEKm5nNpcxOXr0KO+//77qLGbmHHi9Xurq6tTdvxBChaCaKxfz+6PRqMoidrvdDA4OKl+Hw+EgGAxy9OjRZR4FjUazlqloJQAwPDxMQ0MDjY2NzMzMMDg4SD6fVw1cFk7MXq9XmYcmJyeJxWKqf3ChUGBkZIRoNKru/k2lAKgSEdPT08RiMUKhEFVVVSQSCd5//32am5vZsWOHUhojIyPE43G2b99+1Uqhv7+fv/qrvyISieB2uwmFQmzevFmtKux2u0pkW+h7MDuVlUolYrGYSizz+XzE43Hi8bgKfw0GgxiGsZzdxTSaVYk1WItMpSl9JKJQc20qXgmkUim6u7v59Kc/TTabpaenR921d3d3s3v37kXRMRaLhWQyyb333rvI1APlO+xoNMqRI0fo6elhZmaGtrY2kskkExMTDA0NqRj9mpoastmsqgv013/918zNzbFz505lbtqzZ89VCiCVSnH06FFsNhsbNmwgEong9/uVAjCVTTwep1QqKYe2WStpoSmqsbGRZDLJyMgI4XCYeDyu/BehUGg5ykhrNKsa67YtPPTSaf5n335a/58ipZ6LH/+hdU7FKwEom11Onz7NwYMHiUajDAwMIIRQGbZ79+5dVD7BDNH8qPlGCEEgEODRRx9lZmZGOZHdbjd1dXW0traq5DSzFIW5GpidneWtt95iYmKCmpoaQqEQw8PDtLa2KmdwoVDg9OnTTE1NUVdXR2dnJ729vcTjcSKRyKJuZm63W3UkW9iXwKxe6nQ6uXLlChcuXFCmIdNMFAwG8fv9HDlyZHlPvEazijA2tDPyb638Tu0F/nnwA/79n3Xyk091UhjTBRZvxKpQAgCXLl0iEAjQ3t5Of38/s7Oz7N+/H4fDQTKZZPv27TddVjkUCvH000/z1ltvqWxjMxHLjOGfnZ1lenqauro6/H6/Kj0xNjbG2bNnaW5uVpm6GzZsYHZ2llOnTnHp0iVl4jHDOBOJBCMjI6qcRTAYxO12q5BX87vNz8zNzTE8PMypU6cwDAO/368ig2w2Gx0dHVy4cGG5+gtrNKuOwv+1j8/+wY/41aoRrKJ8Q/XPAh/w3//zQdp+NUMxPLvCElYuq0YJAJw7d4729naamppIpVJEIhFcLpeaNKWUtLa2snv3bmpra28Y1VNfX09bWxsDAwNAOUvZDEdNJpNIKVVyWjweVwXccrkcgUBAOXUDgQDRaJTjx48zPDysTDr5fF5VMTVl3LhxoyoeZ9YxMo9jKgOzTMXx48fJZDK0tLRw+XK5CJjdbmfr1q1MTk5y6tSppW4mo9GsSixuN9t/v4cvVo+xMOrdKiycvu+/s/UPnmXjFxLIfO76B1nHrColMDc3x/e//30OHz7Mzp07GR4exu124/f7SSQSpFIphoaGGB0dZefOnWzYsEGFXV6LAwcOkE6nGRsbU6WZTcdtVVWVqjRqViI1J+2dO3dSW1tLZ2cnNpuNEydOMDMzQzabRQhBMpkkmUzi9Xq5cOEChUKBqqoqgsEgDodDJah9NC8hmUzS29vLxYsXyWQy+P1+xsbGSKVSeL1e2tvbcblcHD9+XB1Do1n3CEGtLXnNt6zCQlNtDCxX1x/TlKnoZLFrUSqVOHPmDKVSiXg8rip2Lmzcfv78eY4cOcKRI0eYnJy86hhmTSIoV/psb29XBeUsFotSAoVCQTVskVISj8fZsmULDz30EDt27MDhcDA0NER/f/+iWP3JyUnOnj3L5cuXyeVyKmPYzPA1G9mY0UlmsbrTp0/T29tLPp/H6/USi8VIpVIYhqF6CnR3d6vcB41GA6Vkkr/56kN8YehhsvLDIpBTxSQbXv1VPM+VkNp0el1W1UrAJBKJqBDKixcv4vP5qK2txeVyqVLR5j6RSITPfOYztLe3q7tui8WC1+tVtfsPHjxIU1MTPT09ZDIZXC6XuvMfHR1VPQRSqRSzs7MqvHN4eJjz58+rlUKhUGB6epqZmRmuXLmi7ubr6upIJBJcvnxZOX4bGxvxer1YLBYSiQTHjx/nwoULqpGOWY3UMAy1ojl27BhTU1Mfc3Y0mvWH89V3CZ9s4vAjv8nUk2WzT9MrNu55+RQFbQa6IeKjYZQrIsQt1jaH8kTe1tZGIBAgm81y//3309TURD6fx263Y7PZsNlsKomrq6uL++67T0XnXIt8Ps/Zs2eZmppSdfs/+OADrly5QiAQwO/3UygUCAQC7N69mxMnTjA+Pq7aUk5PT3Pp0iW1yjAznYUQbNq0ierqalwuFw0NDdTX1yOlJJFIcOLECSYmJsjlcszNzZFKpVQ3ta1bt2IYBt3d3czMzNzeCb41Tlyr0fzt4BcBeZ/QvekrgTfkX+pxXYMsxbiuypUAlM1CU1NTqgvXG2+8QV1dHcFgULViNDuPxeNxpqamVMmGtra2RcdKJBJMT08jpaSpqYm9e/eq9zZu3MhLL70EoJq9zMzMcOTIETKZDMVikStXrjA6OkqxWKS+vp7m5maCwaDyF6TTaYQQVFVV0dLSgs/nwzAMZmZm6OnpYWxsjGg0uqhTWlVVFTt37iQSiXDixIlFvQ40S4MwDPr/zQGkBTr/LEbp/fMrLZJGc9dZtUoAyolZvb29PP744zgcDgYGBhgZGVE19quqqpiZmSEQCODz+ejt7WViYoLx8XGam5tpaWlRhdvMOjyjo6MMDg4yPT1NU1MTXV1dVFVV8f7776vVgBneaXb8qqurY2xsjHQ6rXIWzMJ0Dz74IMlkEpvNpiKDTNmHhoaYmppS+Q7ZbJZMJoPP52P//v2MjIxw6tQp7QReJvq/u4OLh/8Qq7Dwr5+8l6OPtVKcnl5psTSau8qqVgJQ7rP75ptvcuDAAerq6pienmZ2dpZcLkdDQwMWi4VNmzYhpVS9BWZmZujr68Nms7F7927VK8BqtVJbW6uctUIIUqkUo6OjTE9P09fXh9/vx+/3U1tbS2NjI+l0mkwmo45tVgI1J24zm9n0QZjHtlqtylRltorMZrN4vV66uroYHx/n5MmTOgx0mTBaW/it3X+rYsq/GrzIQHc3n+v5ZULPhinOhFdYQo3m7rDqlQBAMpnk2LFj3HvvvYRCIcbHx8lkMszMzBAMBjl//jx2ux2/368icszSDD/96U9JpVLs3buXmpoaCoWCShorlUqcOnWKmZkZdWefTCYJh8NYrVZVsO7EiRPkcjlaW1uJRCJ4PB7Vb9g0A5nKwIwWyuVyTE9PMzk5qZzAzc3NbNiwgdHRUU6cOLHCZ3XtYt22Bc8LYX6zZmjR9g02Lyf2fY+dz/46Tf9BF+bTrA/WhBKAcvOVS5cu0d7ezsaNGxkYGCAWi6lM3IGBAUKhkMoZyGQyCCFUuYn33nuPQqFAOp1WtYmsViuTk5Mkk0kVgmoWpIvH40xPT+N0OlVTl6amJmw2m0osA1STGyj7FMzEs4sXL9Lb20smk1G+iEAgwJkzZxgaGkKzPFx6cT+f2HqJ77b/5Lr7PPr59+j/y3YKA3ocNGufNaMEoGwa6u3txev1snnzZkZHRwmHy8t6m83GmTNn2LFjh2o1CaisXvPO3W63q0k9mUySSCRwu914vV5SqRRWq1U5ncPhMC0tLTz66KPKsezz+fB4PKpktWn+MU1GAGfOnGFwcJBYLEY2m6Wqqor6+nqOHz9OPB5fmZO3xrG43fT91y1cPPwtHOL6EWIA32x6jxdea+KHn9iqzUKaNc+aUgJQTgSLxWKMj4+zY8cOBgcHVf/hdDqN1+ulo6MDl8ulCsWZOQGGYagevmbz+PHxcRXNY9rwS6WSsu+n02l8Ph+BQED1LUin06oshFkWOpFIUCwWSSQSKgIokUhQX19PXV0db7zxxlVVTzVLg7Wmhg++2UH/I98GbqwATJ6rGmfqb/z83Re6dNSQZk2z6jKGb5ZwOMzo6ChCCEKhEJlMhqmpKc6fP8/o6CjpdFqFfJoN5c1J3DQZmU1lzNISsVhMJYWZJZ8jkQiDg4NMTEwA5RWHWX8oHo8zOztLOBxmcnKSoaEhNfmHw2EaGhqoqanh+PHjWgEsI4Vt7fQ/9u1b/txXgxdpe2EAy657l0EqjaYy+NiVgBDiReBngSkp5Y75bQHgL4AOYBB4WkoZEeWU3P8EfAZIAf9ISnlyeUS/MblcTtXb9/v91NfXY7fbVeN3M3S0oaFB1fNZ6DSOxWKqs5d5926Wgi4Wiypu31wVRKNRCoUCDodDfdbtdjM1NcXk5KTKMgYIBoMcPnyY2dlZjh49WmmN4juFEL1U6Ljebf645Rif2LQLd89KS6LRLA83Yw76U+APgO8u2PY88KaU8veEEM/Pv/4d4NNA5/zffcC35h9XlHg8TnV1Ndu3b2d6epqRkRFGRkYIBAK0tbVRV1enCsZlMhkGBwcpFArKV2BSLBaVwjDr/5h38Pl8ntHRUaLRqCpPEQ6HsdlsBINB8vk8LpcLr9fL1NQUL7/8sqojVGHMSSk7V8O43g3+LlPCHi+stBgazbLxsUpASvkTIUTHRzY/BTwy//w7wFuUJ4ungO/K8sz4jhCiWgjRKKW8smQS3ybDw8Oq1HNra6uK3jl58iQWiwWn04nf71elnX0+n4r7N4vKAepuHlA1/t1uNxMTE3R3d5Na/S3tTE/oqhjXm8GYjPHs8Cf4dtvbt/S5H6Uc/L//8ln8b76zTJJpNCvP7TqGQwsmgAkgNP+8GRhZsN/o/LaKmSyKxSKDg4M4HA7q6+tVG8dMJsPly5dVXL/FYiEQCCj7v+kDWOg4NvsATE5OcvLkybWgAADM+hSralxvRLFvgIlf28r+A1/iO1/9/9hud33sZ7b+0a9Tf6qA/1WtADRrmzuODpJSytspACeEeA547k6//3bJZrOMjIwghGB2ttx16ODBg6rM9KVLlwgGgzQ2NuJwOJQDGVCKIB6Pk0wm6enpWXO1fZZiXJ24l1yu26V0+gK1p+Gf9DzHV/78zwA46MjgtnzYnzor8xzLOPjyf/si7b//LrKgzUCatc/tKoFJ0xwghGgEzPrGY0Drgv1a5rddhZTyBeAFuL0qokuFlFJN4CdPnuTAgQO0tLTgdDqJRqOMjIyQz+fx+XwEg0GVVWzWGDLzENYQNoClGFe/CFReyNO7Z/h3m3YBcOmPDtLU8WFl1omZKjb/36do4SiVJ7hGszzcrhJ4BXgG+L35x5cXbP8NIcRLlB2HsdViN4ZystlPfvJhJqnX6yUYDJJMlrsWDQ0NKUfx9NotNFY7/7hmxvV6bPniu4teb14hOTSaleRmQkT/nLITOCiEGAW+Rnny/54Q4leAIeDp+d1foxxG2Ec5lPDZZZD5rpFIJFQXr1gsRi63LppT+OdDRNfsuGo0mg+5meigX7rOW1d1lZiPHvnynQpViawTBQBw6aNNKtbyuGo06501mzGs0Wg0mo9HKwGNRqNZx2gloNFoNOsYrQQ0Go1mHaOVgEaj0axjtBLQaDSadYxWAhqNRrOO0UpAo9Fo1jFaCWg0Gs06RisBjUajWcdoJaDRrELOyW7+j3yVY/KI2paXOU7Kn/B38keclD8hL8ulTuZLoLcKIfqEED1CiC7zM0KIZ4QQvfN/z9zt36FZebQS0GhWIU20s5eHFm0b5CIB6nlQfIoA9QxyEYAwEwBOyu1Bn6PcHtTsFf41ypVhDwJfE0LU3LUfoakItBLQaFYhNaIOG/ZF26YZp5F2ABppZ5pxtR0IyzLvANXz/SKeBH4spZyVUkaAHwOfunu/QlMJaCWg0awRcmRxiHLrTDtOcmQByJIuv/0hZnvQ67UN1awj7ri9pEajqTyEECxle7RKbRuquXP0SkCjWSPYcZCVaQCyMo0dBwAOXOW3P8RsD3pLbUOllPullPtt88fVrA2E2Tx9RYUQYg74YKXluA5BYOZj97r7LJdc7VLKuqU4kBBiGkiyvs7fUnCzstkpO3vPzb9uAQrABNBAeaU/ClQB7YCDshP4m1LKg/OO4ROAGS10EtgnpZy90ZdW+P/rzVLJ438zmPLf8f9rpZiDPvhoN6tKQQjRXYmyVapcC5FS1lWqnJUqF9ycbAvavhqUJ/yvAT8Evge0AReBp6WUs0IIAfwBH2kPOv/evwLemz/s1z9OAcxTsf+vN0slj//NsJTyV4oS0Gg0t8BStX2VUr4IvLiEomlWGdonoNFoNOuYSlECL6y0ADegUmWrVLk+SqXKWalyQWXLBpUv382w2n/DkslfEY5hjUaj0awMlbIS0Gg0Gs0KoJWARqPRrGNWXAkIIT4lhPhgvsLh8yssy6AQ4owQ4n0hRPf8toAQ4sfzVRZ/fLcKbAkhXhRCTAkhzi7Ydk1ZRJlvXqtK5EpRSeM6L09FjK0e17vDrYx3JZznpboubqsqrJRyxf4AK9APbKSc+HIa2LaC8gwCwY9s+33g+fnnzwP//i7J8jDlJJ6zHycL8BngfwMCuB84rse1MsdWj2vljXclnOeluC6AAHB5/rFm/nnNx373Cg/UIeD1Ba+/Anylwi6cD4DG+eeNlBNl7pY8HR+5KK4pC/DHwC9daz89rpU3tnpcK2u8K+U83+l1AfwS8McLti/a73p/K20OqrQqhhI4IoQ4MV8wCyAkpbwy/3wCCK2MaDeUpdLOY6XJA5U9tnpcl55bGe9K/V23Ku9t/Q6dMbyYh6SUY0KIeuDHQoiLC9+UUkohREXE1FaSLKuEVTG2lSLHGmBVjPfNspzyrvRK4KarGN4NpJRj849TwA8od1uaFOUGHMw/Tq2UfDeQpaLOI5UnT6WPrR7XJeYWx7tSf9etyntbv2OllcB7QKcQYoMQwg78IvDKSggihPAIIXzmc+AJ4Oy8PKaX/Rng5ZWQb57ryfIK8IX5qIH7gdiCZeRKUDHjCqtibPW4LiG3Md6Vdp5NblXe14EnhBA185FET8xvuzEV4MD5DHCJctTBv1hBOTZSjnY4Tbk077+Y314LvAn0Am8Agbskz58DV4A8Zdver1xPFspRAn84fw7PAPv1uFbm2OpxrbzxroTzvFTXBfDLlKvF9gHP3sx367IRGo1Gs45ZaXOQRqPRaFYQrQQ0Go1mHaOVgEaj0axjtBLQaDSadYxWAhqNRrOO0UpAo9Fo1jFaCWg0Gs065v8HscyVJLLU+sEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "\n",
    "for dir_ in all_data:\n",
    "    process_img, img_affine, ROI_xy  = process(dir_)\n",
    "    \n",
    "    #输出处理信息\n",
    "    print('roi=',ROI_xy)\n",
    "    z = 30\n",
    "    plt.figure()\n",
    "    plt.subplot(1,3,1);plt.imshow(process_img[z,...,0],cmap='gray')\n",
    "    \n",
    "    #process_img(64, 128, 128, 1)\n",
    "    #增加n,z,w,h,c\n",
    "    input_img = tf.expand_dims(process_img,0)\n",
    "    #1,64,128,128,1\n",
    "    \n",
    "    #预测\n",
    "    predict_img = model.predict(input_img)\n",
    "    predict_img = tf.argmax(predict_img, axis=-1)#n,z,w,h\n",
    "    predict_img = tf.squeeze(predict_img)#z,w,h\n",
    "    predict_img = tf.transpose(predict_img, perm=[1,2,0])#w,h,z\n",
    "    #128，128，64\n",
    "    \n",
    "    plt.subplot(1,3,2);plt.imshow(predict_img[...,z])\n",
    "    \n",
    "    #分开predict1：Submandibular、predict2：Parotid\n",
    "    predict1 = predict_img.numpy()\n",
    "    predict2 = predict1.copy()\n",
    "    predict1[predict1 == 2] = 0\n",
    "    predict2[predict2 == 1] = 0\n",
    "    predict2[predict2 == 2] = 1\n",
    "    \n",
    "    #变换回剪裁的ROI大小 双线性变换\n",
    "    predict1 = tf.image.resize(predict1,(ROI_xy[1]-ROI_xy[0],ROI_xy[3]-ROI_xy[2]),method='bilinear')\n",
    "    predict2 = tf.image.resize(predict2,(ROI_xy[1]-ROI_xy[0],ROI_xy[3]-ROI_xy[2]),method='bilinear')\n",
    "    \n",
    "    #二值化\n",
    "    predict1 = predict1.numpy()\n",
    "    predict2 = predict2.numpy()\n",
    "    predict1[predict1 >= 0.5] = 1\n",
    "    predict1[predict1 < 0.5] = 0\n",
    "    predict2[predict2 >= 0.5] = 1\n",
    "    predict2[predict2 < 0.5] = 0\n",
    "    \n",
    "    #叠加\n",
    "    predict_img = predict1 + predict2*2\n",
    "    \n",
    "    #输出out_nii\n",
    "    #将裁剪的ROI放回对应位置，其余位置0填充\n",
    "    out_nii = np.zeros((ROI_xy[4],ROI_xy[4],ROI_xy[6]))\n",
    "    out_nii[ROI_xy[0]:ROI_xy[1],ROI_xy[2]:ROI_xy[3],ROI_xy[5]:ROI_xy[5]+64] = predict_img\n",
    "    \n",
    "    #save seg_nii\n",
    "    name = tf.strings.split(dir_,os.sep)[-1]#0000.nii.gz\n",
    "    name = tf.strings.split(name,'.')[0]#0000\n",
    "    name = name.numpy()\n",
    "    name = name.decode(\"utf-8\")\n",
    "    nib.Nifti1Image(out_nii,img_affine).to_filename('../data0_link/yhs_data/NPC_test_seg/' + str(name) + '_seg.nii.gz')\n",
    "    \n",
    "    plt.subplot(1,3,3);plt.imshow(out_nii[...,z+ROI_xy[5]])\n",
    "    plt.show\n",
    "    print('------------------------finish:',name)\n",
    "\n",
    "print('------------------------all-finish------------------------------')\n",
    "elapsed_time = time.time() - start_time\n",
    "print('time usage: ' + str(timedelta(seconds=int(round(elapsed_time)))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26f8fb2d-3c97-4926-8a54-5c19366aa207",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
